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Catch up on the top AI-related news and research in radiology over the past month.

Irrespective of differences with radiation dosing and tube voltage, obtaining CT-derived fat fraction from photon-counting detector CT provides consistently comparative results to MRI-derived proton density fat fraction assessment, according to new research.

For patients who had neuroradiological trauma CT scans, researchers noted no significant visual differences between deep learning denoising at 25% mAs and iterative reconstruction at 100% mAs.

Catch up on the top radiology content of the past week.

The CINA-CSpine AI software reportedly demonstrated a 90.3 percent sensitivity rate for cervical spine fractures in validation testing on more than 300 non-contrast CT scans.

Emphasizing multi-structure segmentation and feature extraction from chest CT scans, an emerging AI model demonstrated an approximately 70 percent AUC for predicting significant incidental extrapulmonary findings as well as two-year and 10-year all-cause mortality.

Radiomics models offered a pooled AUC of 86 percent for differentiating between ruptured and unruptured intracranial aneurysms, according to a recently published meta-analysis.

Catch up on the top radiology content of the past week.

Two ultralow-dose photon counting CT protocols yielded satisfactory quality for diagnostic assessment in over 95 percent of patients who had post-transplant lung deformities, according to new research.

Catch up on the top radiology content of the past week.

A machine-learning-based model demonstrated an 87 percent area under the curve and a 90 percent specificity rate for predicting interstitial lung abnormality on CT scans, according to new research.

In a study involving 396 confirmed neuroendocrine tumor liver metastases, researchers found that Gd-EOB-DTPA-enhanced MRI offered 19 percent higher sensitivity than polyenergetic CT.

Adjunctive AI showed no difference in accuracy than unassisted radiologists for intracranial hemorrhage (ICH) detection and had a slightly longer mean report turnaround time for ICH-positive cases, according to newly published prospective research.

Catch up on the most-well viewed radiology content in August 2024.

Catch up on the latest research in neuroradiology over the past month.

Catch up on the top AI-related news and research in radiology over the past month.

Catch up on the top radiology content of the past week.

For patients who had post-op recurrence after a Whipple procedure for pancreatic ductal adenocarcinoma (PDAC), between 80 to 86 percent of follow-up CT exams revealed new or increased soft tissue.

The AI-powered qCT LN Quant software reportedly generates 2D and 3D reconstructions and facilitates assessment of morphologic data across multiple thoracic studies.

Key features of the HealthCCSng V2.0 software include numerical coronary artery calcium (CAC) scoring, a new zero CAC category and user customization of upper and lower limits for CAC scoring categories.

Providing an 85.7 percent sensitivity rate for detecting cerebral aneurysms on computed tomography angiography (CTA), the deep learning model also offered a similar AUC (93 percent) in comparison to radiology reports (91 percent).

For patients with stable chest pain, CT angiography demonstrated a nearly 40 percent higher sensitivity rate than exercise electrocardiography and over an 18 percent higher sensitivity rate than SPECT for detecting obstructive coronary artery disease (CAD).

Catch up on the top radiology content of the past week.

While cone-beam computed tomography (CBCT) demonstrated high pooled sensitivity and specificity rates for intraparenchymal hemorrhage in the systematic review, researchers noted higher false-negative rates for subarachnoid and intraventricular hemorrhages.

Utilizing low-dose computed tomography (CT) scans for a cohort of over 10,000 non-smokers, researchers found that over 11 percent of study participants had clinically relevant lung nodules.

In addition to a 20.1 percent reduction in iodine load, photon-counting CT offered higher signal-to-noise and contrast-to-noise ratios than energy-integrating detector CT in a new study.

Catch up on the top radiology content of the past week.

Automated CT-derived assessments may provide stronger stratification of diabetes and cardiometabolic risks than conventional predictive models, according to a new study of over 32,000 adults in Korea.

New research shows the use of adjunctive AI resulted in a 12 percent higher sensitivity rate for lung nodule detection in comparison to radiologists without AI.

Catch up on the top radiology content of the past week.